Recursive feature elimination (RFE)

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Table of contents

1. Introduction 
1.1. SMHI
1.2. Background
1.3. Previous work
1.4. Objective
2. Data 
2.1. Data description
2.2. Data preprocessing
3. Methods 
3.1. Sampling methods
3.2. Random forest regression
3.3. Recursive feature elimination with random forest regression
3.4. The multilayer perceptron
3.4.1. Mini-batch stochastic gradient descent
3.5. Performance evaluation metrics
3.6. Technical aspects
4. Results 
4.1. Recursive feature elimination with random forest regression
4.2. Random forest regression
4.2.1. Results using simple random sampling
4.2.2. Results using stratied random sampling
4.2.3. Results using simple random sampling on ltered data
4.2.4. Results using stratied random sampling on ltered data
4.3. The multilayer perceptron
4.3.1. Results using simple random sampling
4.3.2. Results using stratied random sampling
4.3.3. Results using simple random sampling with a log transformed
4.3.4. Results using stratied random sampling with a log transformed
4.3.5. Results using simple random sampling on ltered data with a log transformed response
4.3.6. Results using stratied random sampling on ltered data with a log transformed response
4.4. Comparison of the multilayer perceptron and random forest model
5. Discussion 
6. Conclusions 
Bibliography 
A. Appendix